Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fb80decbf60>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fb80dec65c0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.5.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='inputs_z')
    learning_rate = tf.placeholder(tf.float32, shape=(None), name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):

        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        x1 = tf.maximum(alpha * x1, x1)
                
        x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        
        flat = tf.reshape(x2, (-1, 7*7*128))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [15]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha=0.2
    with tf.variable_scope('generator', reuse = not is_train):
        x1 = tf.layers.dense(z, 7*7*128)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 128))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 64, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)

        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [16]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [17]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    d_vars = tf.trainable_variables('discriminator')
    g_vars = tf.trainable_variables('generator')
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [18]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [44]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, image_width, image_heigth, image_channels = data_shape
    out_channel_dim =  data_shape[-1]
    
    input_real, input_z, learning_rate_placeholder = model_inputs(image_width, image_heigth, image_channels, z_dim)
        
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
        
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate_placeholder, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate_placeholder: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate_placeholder: learning_rate})

                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                if steps % 25 == 0:    
                    show_generator_output(sess, 25, input_z, out_channel_dim, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [45]:
batch_size = 128
z_dim = 100
learning_rate = 0.01
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.3625... Generator Loss: 3.9984
Epoch 1/2... Discriminator Loss: 5.4255... Generator Loss: 4.6667
Epoch 1/2... Discriminator Loss: 2.2718... Generator Loss: 0.9534
Epoch 1/2... Discriminator Loss: 4.4322... Generator Loss: 0.0488
Epoch 1/2... Discriminator Loss: 1.0267... Generator Loss: 1.8429
Epoch 1/2... Discriminator Loss: 0.3116... Generator Loss: 2.1792
Epoch 1/2... Discriminator Loss: 6.3036... Generator Loss: 0.8966
Epoch 1/2... Discriminator Loss: 3.8223... Generator Loss: 0.0574
Epoch 1/2... Discriminator Loss: 0.1463... Generator Loss: 3.0241
Epoch 1/2... Discriminator Loss: 0.0640... Generator Loss: 3.7052
Epoch 1/2... Discriminator Loss: 0.0868... Generator Loss: 3.1633
Epoch 1/2... Discriminator Loss: 0.0305... Generator Loss: 5.6805
Epoch 1/2... Discriminator Loss: 2.2598... Generator Loss: 1.1141
Epoch 1/2... Discriminator Loss: 0.4614... Generator Loss: 2.9279
Epoch 1/2... Discriminator Loss: 0.6537... Generator Loss: 4.8493
Epoch 1/2... Discriminator Loss: 0.8367... Generator Loss: 1.7335
Epoch 1/2... Discriminator Loss: 0.2267... Generator Loss: 4.0271
Epoch 1/2... Discriminator Loss: 0.1425... Generator Loss: 3.3353
Epoch 1/2... Discriminator Loss: 0.6450... Generator Loss: 3.0827
Epoch 1/2... Discriminator Loss: 2.8940... Generator Loss: 0.1446
Epoch 1/2... Discriminator Loss: 2.9536... Generator Loss: 0.1518
Epoch 1/2... Discriminator Loss: 1.5479... Generator Loss: 0.7016
Epoch 1/2... Discriminator Loss: 1.6950... Generator Loss: 0.3081
Epoch 1/2... Discriminator Loss: 2.1584... Generator Loss: 0.2594
Epoch 1/2... Discriminator Loss: 0.9115... Generator Loss: 1.8759
Epoch 1/2... Discriminator Loss: 1.1653... Generator Loss: 0.5788
Epoch 1/2... Discriminator Loss: 1.0887... Generator Loss: 0.6077
Epoch 1/2... Discriminator Loss: 0.8807... Generator Loss: 1.6718
Epoch 1/2... Discriminator Loss: 0.9482... Generator Loss: 2.0299
Epoch 1/2... Discriminator Loss: 1.0890... Generator Loss: 1.4737
Epoch 1/2... Discriminator Loss: 0.7259... Generator Loss: 1.4830
Epoch 1/2... Discriminator Loss: 0.8270... Generator Loss: 2.1028
Epoch 1/2... Discriminator Loss: 1.4441... Generator Loss: 0.3441
Epoch 1/2... Discriminator Loss: 1.2072... Generator Loss: 3.3564
Epoch 1/2... Discriminator Loss: 0.6594... Generator Loss: 1.0267
Epoch 1/2... Discriminator Loss: 0.7632... Generator Loss: 1.8593
Epoch 1/2... Discriminator Loss: 1.1264... Generator Loss: 0.5065
Epoch 1/2... Discriminator Loss: 0.5756... Generator Loss: 1.9985
Epoch 1/2... Discriminator Loss: 0.5913... Generator Loss: 2.7584
Epoch 1/2... Discriminator Loss: 1.1738... Generator Loss: 2.6393
Epoch 1/2... Discriminator Loss: 0.5035... Generator Loss: 1.3638
Epoch 1/2... Discriminator Loss: 2.2707... Generator Loss: 5.4328
Epoch 1/2... Discriminator Loss: 0.6668... Generator Loss: 3.1943
Epoch 1/2... Discriminator Loss: 0.2976... Generator Loss: 1.6988
Epoch 1/2... Discriminator Loss: 0.2735... Generator Loss: 1.8429
Epoch 1/2... Discriminator Loss: 0.2234... Generator Loss: 2.0415
Epoch 2/2... Discriminator Loss: 0.1159... Generator Loss: 3.2607
Epoch 2/2... Discriminator Loss: 0.1695... Generator Loss: 2.6521
Epoch 2/2... Discriminator Loss: 1.9141... Generator Loss: 0.2986
Epoch 2/2... Discriminator Loss: 1.1263... Generator Loss: 2.6186
Epoch 2/2... Discriminator Loss: 0.7242... Generator Loss: 0.8824
Epoch 2/2... Discriminator Loss: 0.4292... Generator Loss: 1.7476
Epoch 2/2... Discriminator Loss: 0.3954... Generator Loss: 1.4853
Epoch 2/2... Discriminator Loss: 0.3392... Generator Loss: 1.8890
Epoch 2/2... Discriminator Loss: 0.3151... Generator Loss: 2.2156
Epoch 2/2... Discriminator Loss: 1.6774... Generator Loss: 0.2825
Epoch 2/2... Discriminator Loss: 0.5246... Generator Loss: 1.6797
Epoch 2/2... Discriminator Loss: 2.2768... Generator Loss: 5.0018
Epoch 2/2... Discriminator Loss: 0.7614... Generator Loss: 0.9082
Epoch 2/2... Discriminator Loss: 0.4060... Generator Loss: 1.5287
Epoch 2/2... Discriminator Loss: 0.3098... Generator Loss: 1.7777
Epoch 2/2... Discriminator Loss: 0.3435... Generator Loss: 1.7224
Epoch 2/2... Discriminator Loss: 0.1710... Generator Loss: 2.6493
Epoch 2/2... Discriminator Loss: 0.2164... Generator Loss: 2.4103
Epoch 2/2... Discriminator Loss: 0.0669... Generator Loss: 4.5465
Epoch 2/2... Discriminator Loss: 3.8242... Generator Loss: 5.1380
Epoch 2/2... Discriminator Loss: 1.6619... Generator Loss: 2.2789
Epoch 2/2... Discriminator Loss: 1.1804... Generator Loss: 1.9364
Epoch 2/2... Discriminator Loss: 1.7761... Generator Loss: 0.2817
Epoch 2/2... Discriminator Loss: 1.0698... Generator Loss: 0.5605
Epoch 2/2... Discriminator Loss: 0.4927... Generator Loss: 1.3967
Epoch 2/2... Discriminator Loss: 0.4382... Generator Loss: 1.9912
Epoch 2/2... Discriminator Loss: 0.7807... Generator Loss: 0.7781
Epoch 2/2... Discriminator Loss: 0.6775... Generator Loss: 1.0152
Epoch 2/2... Discriminator Loss: 1.5462... Generator Loss: 0.3164
Epoch 2/2... Discriminator Loss: 0.5813... Generator Loss: 1.7721
Epoch 2/2... Discriminator Loss: 0.4163... Generator Loss: 1.7665
Epoch 2/2... Discriminator Loss: 0.4778... Generator Loss: 1.3390
Epoch 2/2... Discriminator Loss: 0.2466... Generator Loss: 2.1098
Epoch 2/2... Discriminator Loss: 0.1376... Generator Loss: 2.7419
Epoch 2/2... Discriminator Loss: 0.0961... Generator Loss: 2.9125
Epoch 2/2... Discriminator Loss: 0.0301... Generator Loss: 4.6308
Epoch 2/2... Discriminator Loss: 0.0222... Generator Loss: 5.2225
Epoch 2/2... Discriminator Loss: 0.1009... Generator Loss: 3.5242
Epoch 2/2... Discriminator Loss: 0.0294... Generator Loss: 4.7087
Epoch 2/2... Discriminator Loss: 0.0513... Generator Loss: 3.5342
Epoch 2/2... Discriminator Loss: 0.0830... Generator Loss: 3.0354
Epoch 2/2... Discriminator Loss: 0.1814... Generator Loss: 2.2199
Epoch 2/2... Discriminator Loss: 0.0349... Generator Loss: 7.3019
Epoch 2/2... Discriminator Loss: 0.0086... Generator Loss: 7.0875
Epoch 2/2... Discriminator Loss: 0.2280... Generator Loss: 2.0571
Epoch 2/2... Discriminator Loss: 1.1483... Generator Loss: 1.9587
Epoch 2/2... Discriminator Loss: 0.8418... Generator Loss: 0.8636

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [46]:
batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.9633... Generator Loss: 0.3287
Epoch 1/1... Discriminator Loss: 3.0473... Generator Loss: 0.0818
Epoch 1/1... Discriminator Loss: 0.8016... Generator Loss: 0.8574
Epoch 1/1... Discriminator Loss: 0.6703... Generator Loss: 0.9612
Epoch 1/1... Discriminator Loss: 0.1458... Generator Loss: 3.2128
Epoch 1/1... Discriminator Loss: 3.3992... Generator Loss: 11.1670
Epoch 1/1... Discriminator Loss: 0.6679... Generator Loss: 5.2348
Epoch 1/1... Discriminator Loss: 0.2861... Generator Loss: 6.4299
Epoch 1/1... Discriminator Loss: 1.7657... Generator Loss: 0.2419
Epoch 1/1... Discriminator Loss: 0.5073... Generator Loss: 1.6319
Epoch 1/1... Discriminator Loss: 0.1548... Generator Loss: 2.5994
Epoch 1/1... Discriminator Loss: 2.6306... Generator Loss: 0.1539
Epoch 1/1... Discriminator Loss: 0.2179... Generator Loss: 2.7829
Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.3908
Epoch 1/1... Discriminator Loss: 0.5384... Generator Loss: 1.1342
Epoch 1/1... Discriminator Loss: 0.9262... Generator Loss: 0.9061
Epoch 1/1... Discriminator Loss: 1.4294... Generator Loss: 0.3434
Epoch 1/1... Discriminator Loss: 0.1528... Generator Loss: 5.6702
Epoch 1/1... Discriminator Loss: 0.3497... Generator Loss: 8.7164
Epoch 1/1... Discriminator Loss: 1.1926... Generator Loss: 0.4603
Epoch 1/1... Discriminator Loss: 0.2020... Generator Loss: 7.2045
Epoch 1/1... Discriminator Loss: 0.3355... Generator Loss: 5.8688
Epoch 1/1... Discriminator Loss: 0.2545... Generator Loss: 3.7472
Epoch 1/1... Discriminator Loss: 1.3684... Generator Loss: 0.5794
Epoch 1/1... Discriminator Loss: 1.2523... Generator Loss: 0.5722
Epoch 1/1... Discriminator Loss: 1.6449... Generator Loss: 0.3004
Epoch 1/1... Discriminator Loss: 1.6775... Generator Loss: 3.1833
Epoch 1/1... Discriminator Loss: 1.4653... Generator Loss: 0.3699
Epoch 1/1... Discriminator Loss: 0.3465... Generator Loss: 4.8611
Epoch 1/1... Discriminator Loss: 0.3874... Generator Loss: 5.3101
Epoch 1/1... Discriminator Loss: 0.2995... Generator Loss: 2.5925
Epoch 1/1... Discriminator Loss: 0.4287... Generator Loss: 2.3557
Epoch 1/1... Discriminator Loss: 0.8830... Generator Loss: 4.8347
Epoch 1/1... Discriminator Loss: 0.4612... Generator Loss: 1.4542
Epoch 1/1... Discriminator Loss: 0.3936... Generator Loss: 2.6050
Epoch 1/1... Discriminator Loss: 0.2773... Generator Loss: 6.0145
Epoch 1/1... Discriminator Loss: 0.2526... Generator Loss: 5.8171
Epoch 1/1... Discriminator Loss: 1.4649... Generator Loss: 5.9174
Epoch 1/1... Discriminator Loss: 0.6551... Generator Loss: 2.4280
Epoch 1/1... Discriminator Loss: 1.1207... Generator Loss: 0.5140
Epoch 1/1... Discriminator Loss: 0.4081... Generator Loss: 4.2094
Epoch 1/1... Discriminator Loss: 0.3373... Generator Loss: 6.7903
Epoch 1/1... Discriminator Loss: 0.1557... Generator Loss: 8.0430
Epoch 1/1... Discriminator Loss: 0.3483... Generator Loss: 5.0942
Epoch 1/1... Discriminator Loss: 0.9555... Generator Loss: 0.6836
Epoch 1/1... Discriminator Loss: 1.0019... Generator Loss: 1.0932
Epoch 1/1... Discriminator Loss: 0.5146... Generator Loss: 6.9976
Epoch 1/1... Discriminator Loss: 0.3232... Generator Loss: 3.1143
Epoch 1/1... Discriminator Loss: 0.3997... Generator Loss: 3.1780
Epoch 1/1... Discriminator Loss: 0.3174... Generator Loss: 5.0751
Epoch 1/1... Discriminator Loss: 1.4988... Generator Loss: 1.4588
Epoch 1/1... Discriminator Loss: 0.3138... Generator Loss: 3.2684
Epoch 1/1... Discriminator Loss: 0.1726... Generator Loss: 3.4861
Epoch 1/1... Discriminator Loss: 0.5608... Generator Loss: 6.8792
Epoch 1/1... Discriminator Loss: 0.7935... Generator Loss: 0.7714
Epoch 1/1... Discriminator Loss: 0.3909... Generator Loss: 1.9487
Epoch 1/1... Discriminator Loss: 0.4648... Generator Loss: 1.7685
Epoch 1/1... Discriminator Loss: 0.7339... Generator Loss: 0.9736
Epoch 1/1... Discriminator Loss: 1.7389... Generator Loss: 0.9058
Epoch 1/1... Discriminator Loss: 0.3957... Generator Loss: 3.6209
Epoch 1/1... Discriminator Loss: 1.0087... Generator Loss: 0.7946
Epoch 1/1... Discriminator Loss: 1.0008... Generator Loss: 0.6700
Epoch 1/1... Discriminator Loss: 0.9702... Generator Loss: 0.7127
Epoch 1/1... Discriminator Loss: 0.4591... Generator Loss: 4.2315
Epoch 1/1... Discriminator Loss: 0.2741... Generator Loss: 3.2001
Epoch 1/1... Discriminator Loss: 1.5293... Generator Loss: 1.7828
Epoch 1/1... Discriminator Loss: 0.7629... Generator Loss: 0.9423
Epoch 1/1... Discriminator Loss: 1.3480... Generator Loss: 0.4265
Epoch 1/1... Discriminator Loss: 0.4778... Generator Loss: 2.1049
Epoch 1/1... Discriminator Loss: 0.4391... Generator Loss: 3.2824
Epoch 1/1... Discriminator Loss: 1.2426... Generator Loss: 0.6078
Epoch 1/1... Discriminator Loss: 0.5221... Generator Loss: 1.5571
Epoch 1/1... Discriminator Loss: 0.8945... Generator Loss: 0.7281
Epoch 1/1... Discriminator Loss: 0.9585... Generator Loss: 0.7128
Epoch 1/1... Discriminator Loss: 3.3363... Generator Loss: 1.9710
Epoch 1/1... Discriminator Loss: 0.4910... Generator Loss: 1.9676
Epoch 1/1... Discriminator Loss: 0.4300... Generator Loss: 3.1195
Epoch 1/1... Discriminator Loss: 0.2177... Generator Loss: 4.2860
Epoch 1/1... Discriminator Loss: 0.7275... Generator Loss: 1.0336
Epoch 1/1... Discriminator Loss: 0.5928... Generator Loss: 1.6222
Epoch 1/1... Discriminator Loss: 0.6781... Generator Loss: 1.3278
Epoch 1/1... Discriminator Loss: 0.4853... Generator Loss: 2.6921
Epoch 1/1... Discriminator Loss: 0.4993... Generator Loss: 1.7449
Epoch 1/1... Discriminator Loss: 0.5269... Generator Loss: 1.6746
Epoch 1/1... Discriminator Loss: 0.3724... Generator Loss: 2.6788
Epoch 1/1... Discriminator Loss: 2.4634... Generator Loss: 3.7727
Epoch 1/1... Discriminator Loss: 2.9463... Generator Loss: 0.1113
Epoch 1/1... Discriminator Loss: 2.4239... Generator Loss: 2.4823
Epoch 1/1... Discriminator Loss: 0.7637... Generator Loss: 2.5013
Epoch 1/1... Discriminator Loss: 0.9719... Generator Loss: 1.9919
Epoch 1/1... Discriminator Loss: 1.0820... Generator Loss: 3.0666
Epoch 1/1... Discriminator Loss: 1.5995... Generator Loss: 1.2322
Epoch 1/1... Discriminator Loss: 0.3348... Generator Loss: 6.2500
Epoch 1/1... Discriminator Loss: 0.9852... Generator Loss: 0.8368
Epoch 1/1... Discriminator Loss: 1.0528... Generator Loss: 0.6588
Epoch 1/1... Discriminator Loss: 0.3740... Generator Loss: 2.5671
Epoch 1/1... Discriminator Loss: 0.8492... Generator Loss: 0.8317
Epoch 1/1... Discriminator Loss: 0.2944... Generator Loss: 3.1539
Epoch 1/1... Discriminator Loss: 0.4729... Generator Loss: 1.6497
Epoch 1/1... Discriminator Loss: 1.0840... Generator Loss: 1.0443
Epoch 1/1... Discriminator Loss: 0.6176... Generator Loss: 2.8160
Epoch 1/1... Discriminator Loss: 1.0042... Generator Loss: 0.7271
Epoch 1/1... Discriminator Loss: 0.6414... Generator Loss: 1.5302
Epoch 1/1... Discriminator Loss: 0.4237... Generator Loss: 4.4108
Epoch 1/1... Discriminator Loss: 0.7722... Generator Loss: 1.0332
Epoch 1/1... Discriminator Loss: 0.1690... Generator Loss: 3.4363
Epoch 1/1... Discriminator Loss: 0.2599... Generator Loss: 2.7770
Epoch 1/1... Discriminator Loss: 0.3347... Generator Loss: 5.3172
Epoch 1/1... Discriminator Loss: 2.3719... Generator Loss: 0.5107
Epoch 1/1... Discriminator Loss: 1.8768... Generator Loss: 1.8732
Epoch 1/1... Discriminator Loss: 1.3468... Generator Loss: 1.2354
Epoch 1/1... Discriminator Loss: 0.9249... Generator Loss: 1.6671
Epoch 1/1... Discriminator Loss: 1.7286... Generator Loss: 1.4869
Epoch 1/1... Discriminator Loss: 0.9693... Generator Loss: 0.7390
Epoch 1/1... Discriminator Loss: 0.6190... Generator Loss: 1.7210
Epoch 1/1... Discriminator Loss: 0.8719... Generator Loss: 1.5570
Epoch 1/1... Discriminator Loss: 1.5177... Generator Loss: 0.4729
Epoch 1/1... Discriminator Loss: 1.1443... Generator Loss: 2.0667
Epoch 1/1... Discriminator Loss: 0.5524... Generator Loss: 2.1163
Epoch 1/1... Discriminator Loss: 0.5151... Generator Loss: 4.0244
Epoch 1/1... Discriminator Loss: 1.7229... Generator Loss: 1.5843
Epoch 1/1... Discriminator Loss: 0.7255... Generator Loss: 1.0905
Epoch 1/1... Discriminator Loss: 0.9842... Generator Loss: 1.3036
Epoch 1/1... Discriminator Loss: 1.7055... Generator Loss: 3.4790
Epoch 1/1... Discriminator Loss: 0.6580... Generator Loss: 1.2841
Epoch 1/1... Discriminator Loss: 1.0365... Generator Loss: 0.6341
Epoch 1/1... Discriminator Loss: 0.5829... Generator Loss: 1.8004
Epoch 1/1... Discriminator Loss: 0.4040... Generator Loss: 3.0998
Epoch 1/1... Discriminator Loss: 1.1276... Generator Loss: 1.4071
Epoch 1/1... Discriminator Loss: 2.1719... Generator Loss: 1.6959
Epoch 1/1... Discriminator Loss: 0.5126... Generator Loss: 2.1650
Epoch 1/1... Discriminator Loss: 1.5887... Generator Loss: 3.8479
Epoch 1/1... Discriminator Loss: 1.2137... Generator Loss: 0.7916
Epoch 1/1... Discriminator Loss: 0.4997... Generator Loss: 3.3728
Epoch 1/1... Discriminator Loss: 1.3343... Generator Loss: 1.0320
Epoch 1/1... Discriminator Loss: 0.9503... Generator Loss: 0.7612
Epoch 1/1... Discriminator Loss: 0.3747... Generator Loss: 2.4435
Epoch 1/1... Discriminator Loss: 0.6813... Generator Loss: 1.2729
Epoch 1/1... Discriminator Loss: 0.7192... Generator Loss: 1.2101
Epoch 1/1... Discriminator Loss: 0.3842... Generator Loss: 2.2169
Epoch 1/1... Discriminator Loss: 2.6286... Generator Loss: 1.8317
Epoch 1/1... Discriminator Loss: 0.5556... Generator Loss: 2.4031
Epoch 1/1... Discriminator Loss: 1.4669... Generator Loss: 0.6012
Epoch 1/1... Discriminator Loss: 0.5686... Generator Loss: 1.3283
Epoch 1/1... Discriminator Loss: 1.1177... Generator Loss: 0.7100
Epoch 1/1... Discriminator Loss: 1.0367... Generator Loss: 1.3314
Epoch 1/1... Discriminator Loss: 0.3840... Generator Loss: 2.1571
Epoch 1/1... Discriminator Loss: 0.3119... Generator Loss: 3.5599
Epoch 1/1... Discriminator Loss: 0.4314... Generator Loss: 2.0227
Epoch 1/1... Discriminator Loss: 0.7526... Generator Loss: 1.6447
Epoch 1/1... Discriminator Loss: 0.3042... Generator Loss: 2.6949
Epoch 1/1... Discriminator Loss: 0.2872... Generator Loss: 2.3627
Epoch 1/1... Discriminator Loss: 2.7060... Generator Loss: 3.7606
Epoch 1/1... Discriminator Loss: 1.5858... Generator Loss: 0.4349
Epoch 1/1... Discriminator Loss: 0.8222... Generator Loss: 0.9584
Epoch 1/1... Discriminator Loss: 1.3970... Generator Loss: 0.4128
Epoch 1/1... Discriminator Loss: 1.2101... Generator Loss: 1.3903
Epoch 1/1... Discriminator Loss: 1.0029... Generator Loss: 1.1423

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.